Decision Support Systems (DSS) are a class of information processing systems that aim at supporting humans with making decisions when solving complicated problems. They are applied in many fields, including medical diagnostics, integrated circuit design, business, finance and more. DSSs are typically designed to provide users with near real-time access to complex bodies of knowledge. Often, the users of DSSs are themselves experts in the bodies of knowledge at which the DSS is targeted, and these users harness the DSS to better apply their knowledge to a particular set of facts. Thus, conventional DSSs accept a set of facts and, based on these facts and the content of the pertinent body of knowledge, provide potential conclusions drawn from application of the pertinent body of knowledge.
There are several ways to classify DSS applications. Not every DSS fits neatly into one of the categories, but may be a mix of two or more architectures. Holsapple and Whinston (1996) classify DSS into the following six frameworks: Text-oriented DSS, Database-oriented DSS, Spreadsheet-oriented DSS, Solver-oriented DSS, Rule-oriented DSS, and Compound DSS. See Holsapple, C.W., and A. B.
Whinston. (1996). Decision Support Systems: A Knowledge-Based Approach. St. Paul: West Publishing.
DSSs are commonly designed with a bottom-up approach, in which data such as facts, assumptions and/or factors are input into the system, which processes them vis-à-vis its database and outputs a suggested decision.
The foregoing examples of the related art and limitations related therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the figures.